Insights

Shifting Marketing and Sales Efforts From Art to Science

Business Issue

Tapping the power of data

Marketing and sales have always been part art and part science. On one side are the soft skills, such as relationship building; on the other, quantitative data-driven activities, such as account valuation and lead generation. Science, specifically data analytics and machine learning, plays more and more of a role, especially in complex issues such as identifying intent to purchase, sales funnel management, and cross-selling and up-selling. Online B2C organizations have created enviable recommendation engines, and while B2B companies have yet to achieve the same level of sophistication, the payoff for the latter;thanks to the potential sale size—can be much higher.

The Problem

Replacing conventional methodologies

Recognizing the potential that existed, a global telecom firm sought to transform the way it targeted cross-selling and up-selling opportunities for clients. Even though it offers 40-plus products to an account base of more than 1,400 clients worldwide, account managers frequently targeted accounts based not on data, but rather on intuition, historical relationships or the direct solicitation of clients. Given its size, the company needed to develop a recommendation engine that would identify which products its salespeople should offer, and in which order, for best results. In short, it needed to shift its emphasis from art to science.

Multiple issues complicated its efforts. Many clients purchased new products infrequently, and had lengthy purchasing cycles, specific purchasing needs and complex selling requirements. Worst of all, the telecom company lacked historical sales data. Predicting product probability with conventional methodologies such as logistic regression is not easy when there is insufficient data. The organization needed a collaborative filtering strategy as a means of identifying where product gaps existed in order to improve account targeting.

The Solution

Doubling the sales strategy’s effectiveness

ZS developed an algorithm over a six-month period that allowed the telecom company to target each of its accounts with a series of recommendations based on probability of purchase and revenue potential.

The project was hampered by the lack of historical benchmarks. ZS had to first build a model to test predicted products against the firm’s CRM data, using products that had been sold but not reflected as accrued revenue. This allowed an analysis of how well the model was predicting new product sales compared to CRM information. Then it had to establish a benchmark to quantify improvement.

Using the test scenario, the telecom company collected and analyzed six months of post-implementation data. The results showed that the algorithm could pinpoint twice the number of potential upsells identified by the current system. It proved to be similarly effective in implementation.

The machine-learning algorithm can further improve the accuracy of the model as the system adds more data elements. For example, intent-to-purchase dimensions could be built into the algorithm by picking up signals from the Web, social data, blogs or job postings, as well as by applying other techniques such as market basket analysis.

The Results

Boosting revenues through better modeling

When the telecom company applied the algorithm to actual sales, it determined that of the new sales opportunities identified, 63% came from the new algorithm. This represented 56% of new revenues; of the telecom firm’s annual revenue growth, 3% came from the recommendation engine.
With increased availability of quantifiable business data, the potential to identify opportunities and drive profitable revenue growth is greater than ever. Sales and marketing always will require an element of art, but the telecom firm’s use of advanced analytics and machine learning has become its new standard. The proliferation of reliable and actionable data means that organizations are empowered to make decisions based more on facts than on intuition.